| Kurzbeschreibung | In statistics, we are used to search for the best predictors of some random variable. In many situations, however, we are interested in predicting a system's behavior under manipulations. For such an analysis, we require knowledge about the underlying causal structure of the system. In this course, we study concepts and theory behind causal inference. |
| Lernziel | After this course, you should be able to
- understand the language and concepts of causal inference
- know the assumptions under which one can infer causal relations from observational and/or interventional data
- describe and apply different methods for causal structure learning
- given data and a causal structure, derive causal effects and predictions of interventional experiments |
| Inhalt | The material covered in this course has a significant overlap with the
material that has been covered in 401-3620-22L Student Seminar in
Statistics: Causality FS2023. |
| Literatur | Parts of this course will be based on the book "Elements of Causal Inference" (MIT Press, open access). More details will follow. |
| Voraussetzungen / Besonderes | Prerequisites: basic knowledge of probability theory and regression |
Kompetenzen | | Fachspezifische Kompetenzen | Konzepte und Theorien | geprüft | | Verfahren und Technologien | geprüft |
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Leistungskontrolle als Semesterkurs